RLNS regularizes LNS to perform block Gibbs sampling under entropy, interpolating between pseudolikelihood and exact MLE for differentiable combinatorial optimization.
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Presents a fail-closed certification protocol for determining when forecasting leaderboard winners are deployment-actionable, using a traffic dataset to show friction-induced reversals and an audit to prevent overclaiming.
Derives a closed-form task-specific strictly proper scoring rule for ATE estimation by matching local curvature of the IPW error metric.
A tutorial reviewing why traditional prediction models often fail to improve decision quality in stochastic optimization and summarizing key properties and tools of decision-focused learning.
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.
citing papers explorer
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Regularized Large Neighborhood Search
RLNS regularizes LNS to perform block Gibbs sampling under entropy, interpolating between pseudolikelihood and exact MLE for differentiable combinatorial optimization.
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From Forecasting Leaderboards to Deployment Decisions: A Fail-Closed Certification Protocol
Presents a fail-closed certification protocol for determining when forecasting leaderboard winners are deployment-actionable, using a traffic dataset to show friction-induced reversals and an audit to prevent overclaiming.
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Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
Derives a closed-form task-specific strictly proper scoring rule for ATE estimation by matching local curvature of the IPW error metric.
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Decision-Focused Learning: When and Why Traditional Prediction Models Fail
A tutorial reviewing why traditional prediction models often fail to improve decision quality in stochastic optimization and summarizing key properties and tools of decision-focused learning.
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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.